# oneDNN v3.14.2 improves FP8 support for Intel AVX-512 and fixes Intel GPU errors

> **Key takeaway:** This patch release enhances 8-bit floating point (FP8) stability and performance for Intel AVX-512 processors and resolves correctness issues on Intel GPUs.

**Published:** 2026-11-04T08:36:47+00:00 
**Source:** UXL Foundation / oneAPI oneDNN 
**Category:** ai-local-edge 
**Canonical URL:** https://fosswire.org/news/onednn-v3142-improves-fp8-support-for-intel-avx-512-and-fixe.html 

## Summary
The UXL Foundation has released version 3.14.2 of oneDNN, a patch update to the previous 3.14.1 release. This update focuses on improving the reliability and functionality of 8-bit floating point (FP8) operations, which are critical for modern AI workloads and efficient inference.

The release specifically targets stability for Intel AVX-512 enabled processors and Intel GPUs. It addresses several runtime crashes and correctness issues related to matrix multiplication, convolutions, and Scaled Dot Product Attention (SDPA) subgraphs.

## Technical improvements in oneDNN v3.14.2
- **FP8 Optimization:** Added support for f8 destination in layer normalization forward propagation specifically for processors utilizing the Intel AVX-512 instruction set.
- **Stability Fixes:** Resolved runtime errors and system crashes occurring during matmul and convolution operations when using f8_e4m3 or f8_e5m2 binary post-operations on AVX-512 hardware.
- **GPU Correctness:** Fixed a specific correctness issue within the SDPA (Scaled Dot Product Attention) subgraph when handling a query length of 16 on Intel GPUs.
- **Test Suite Cleanup:** Removed benchdnn test cases involving mish post-ops and dynamic scales to prevent false positive correctness reports.

## Why it matters
By stabilizing FP8 formats like e4m3 and e5m2, this update enables more reliable low-precision AI inference on Intel CPU architectures. The fixes for Intel GPUs ensure that developers building AI models using Scaled Dot Product Attention encounter fewer mathematical errors during execution.